次網格尺度海氣象因子空間變異性及其統計參數化

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：24

、訪客IP：13.59.62.2

姓名

鄭安(An Cheng) 查詢紙本館藏

畢業系所

水文與海洋科學研究所

論文名稱

次網格尺度海氣象因子空間變異性及其統計參數化

相關論文

★ 西北太平洋長期波候變遷之研究	★ 近岸海洋波浪對海面粗糙度之影響
★ 濁水溪河口懸浮沉積物輸送之調查研究	★ 低掠角微波雷達海面背向散射強度受波浪影響程度之探討
★ 澎湖海域潮流之數值模擬及其發電潛能評估	★ 台灣沿海表面風之週期特性
★ 微波雷達與CCD影像分析於潮間帶地形測量之應用	★ The directional spreading of surface wave in the shallow water zone
★ Resuspension of bottom sediment on Inner shelf - A case study of North-western coast of Taiwan	★ 平緩海灘表層含水量變化特性研究
★ Development of S-band and Coherent-on-Receive Marine Radar for Ocean Surface Wave and Current Measurement	★ 內陸棚及河口混合與擴散特性觀測研究
★ 臺灣海峽海洋塑料垃圾的輸運	★ 有限項目的連續水質監測應用於探討觀新藻礁區水體環境即時變化
★ 海岸帶地區海表拖曳係數與海表粗糙度（均方傾度）之相依關係	★ 應用微波雷達監測海流之演算法流程改善

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本研究之目的在於定量觀測風、波浪與表面海流特徵於次網格尺度的空間變異性。本研究使用76顆漂流式微型資料浮球，於2021年9月22日起為期一個月在東海南部布放，觀測區域內表面流速、示性波高、平均週期與海表風速等海氣象參數，浮標等間距布放，形成一個空間陣列，每30分鐘並將數據透過衛星回傳，後續對其進行時序上之資料品管。
本文討論海氣象因子觀測數據之定律性(Deterministic)過程與儀器相互誤差及自然界中隨機(Random)特性的比例。首先比較海洋現場觀測實驗資料與衛星遙測資料及數值模式結果之差異，不同資料來源海氣象因子的時空變化趨勢一致，但是海上實測資料呈現更高的空間變異性，討論研究區域海氣象參數變異與空間尺度(Length Scale)的關係。
在海表風、波浪參數方面進行統計分析，發現變異特徵隨空間尺度增加而增加，其中變異係數(C.O.V.)與空間尺度的關係與前人研究進行比較發現有一致的趨勢，較前人研究的空間尺度涵蓋範圍更廣達O(1)~O(2)公里(10~400公里)。並發現數值模式資料於次網格尺度的空間變異性與實際的觀測有差異，各海氣象因子空間變異性，實際觀測與數值模式的比值最大達6至17.9，此比值關係隨空間尺度的增加而降低。進一步利用機率分布擬合，以半常態分布代表不考慮儀器觀測誤差、韋伯分布代表考慮儀器觀測誤差的空間變異機率特性參數化方法。
在海表流的方面，本研究採用兩種拉格朗日(Lagrangian)分析方法針對海表流進行分析，一為利用浮標位置計算在研究區域內的水平延散係數(Dispersion coefficient)；二為計算所對應之有限大小李雅普諾夫指數 (FSLE，Finite Size Lyapunov Exponent)特性。本文比對有限大小李雅普諾夫指數並與海洋擴散條件與空間尺度之關係，結果顯示，研究期間之東海南部海域，擴散距離(δ)於10~100公里李雅普諾夫指數依循δ-2/3斜率、延散係數依循δ4/3斜率，皆呈現理查森定律(Richardson’s Law)之紊流擴散特性，與Corrado et al. (2017)描述於北太平洋環流之擴散特徵差異不大。
海氣象參數實際觀測數值的空間變異性參數化結果，可以提供數值模式、遙測資料在次網格尺度下數據驗證及品管的依據。

摘要(英)

The purpose of this study is to quantify the spatial variability of wind, wave, and surface current features at the subgrid scale. In this study, 76 miniature wave buoys were deployed in the southern part of the East China Sea for one month starting from September 22, 2021, to observe the surface current velocity, significant wave height, mean period, and surface wind speed and other meteorological parameters in the area.
In this study, we discuss the ratio of the Deterministic process to the mutual error of the instruments and the random characteristics of nature in the observations of meteorological factors. We first compare the differences between the experimental data and the satellite remote sensing data and numerical model results. The temporal and spatial variability of the meteorological factors from different data sources are consistent, but the spatial variability of the measured data is higher.
Further statistical analysis of the surface wind and wave parameters reveals that the variability characteristics increase with the spatial scale, and the relationship between the coefficient of variation (C.O.V.) and the spatial scale is consistent with previous studies. The spatial variability of the numerical model data at the subgrid scale is found to be different from the actual observations, and the spatial variability of each meteorological factor, the ratio between the actual observations and the numerical model is up to 6-17.9, and this ratio decreases with the increase of the spatial scale. The spatial variability of the spatial variability is parameterized by using a half Normal distribution for the spatial variability without considering the instrumentation error and a Weibull distribution for the spatial variability with considering the instrumentation error.
For the surface currents, two Lagrangian analysis methods are used to analyze the surface currents: one is to calculate the horizontal dispersion coefficient in the study area using the buoy array position; the other is to calculate the corresponding Finite Size Lyapunov Exponent (FSLE). In this paper, we compare the finite-size Lyapunov exponent with the spatial scale of the ocean dispersion conditions, and the results show that the dispersion distance (δ) in the southern part of the East China Sea during the study period follows the slope of δ-2/3 and the dispersion coefficient follows the slope of δ4/3, both of which exhibit Richardson′s Law. The diffusion characteristics of the turbulent flow according to Richardson′s Law are similar with described by Corrado et al. (2017) for the North Pacific.
The results of spatial variability parameterization of meteorological parameters can provide the basis for data validation and quality control of numerical models and remote sensing at subgrid scale.

關鍵字(中)

★ 微型資料浮球
★ 海氣象參數
★ 空間變異性

關鍵字(英)

★ miniature wave buoy
★ meteorological parameters
★ spatial variability

論文目次

中文摘要 IV
Abstract VI
致謝 VIII
目錄 IX
圖目錄 XI
表目錄 XXII
符號表 XXIII
1 緒論 1
1.1 海氣象因子觀測重要性 1
1.2 海氣象因子空間變異特性 1
1.3 研究目的 2
2 研究資料來源 4
2.1 微型資料浮球 4
2.1.1 微型資料浮球架構 4
2.1.2 微型資料浮球波浪參數計算方法 7
2.1.3 海表粗糙度與風速關係建立 8
2.1.4 微型資料浮球率定 10
2.1.5 2021東海海氣象因子觀測實驗資料品管 15
2.2 ERA5 18
2.3 GOWAF 19
2.4 UMWM 19
2.5 ASCAT 22
3 東海實驗海氣象觀測結果及比較 23
3.1 實際觀測資料空間特性 23
3.2 實際觀測與模式及遙測資料比較 60
3.3 實際觀測與模式及遙測資料比較結果 61
3.4 小結 72
4 風速、波浪空間變異性分析 73
4.1 風速、波浪分析方法 74
4.2 結果 77
4.3 小結 124
5 海流空間變異性分析 125
5.1 海流分析方法 125
5.2 結果與討論 132
5.3 小結 134
6 結論與建議 136
6.1 結論 136
6.2 建議 137
7 參考文獻 138

參考文獻

Apel, J. R. (1994). An improved model of the ocean surface wave vector spectrum and its effects on radar backscatter. Journal of Geophysical Research: Oceans, 99(C8), 16269-16291.
Artale, V., Boffetta, G., Celani, A., Cencini, M., & Vulpiani, A. (1997). Dispersion of passive tracers in closed basins: Beyond the diffusion coefficient. Physics of Fluids, 9(11), 3162-3171.
Ashton, I., Saulnier, J., & Smith, G. (2013). Spatial variability of ocean waves, from in-situ measurements. Ocean Engineering, 57, 83-98.
Aurell, E., Boffetta, G., Crisanti, A., Paladin, G., & Vulpiani, A. (1997). Predictability in the large: an extension of the concept of Lyapunov exponent. Journal of Physics A: Mathematical and General, 30(1), 1.
Babiano, A., Basdevant, C., Le Roy, P., & Sadourny, R. (1990). Relative dispersion in two-dimensional turbulence. Journal of Fluid Mechanics, 214, 535-557. doi: 10.1017/S0022112090000258
Barrett, S., Ashton, I., Lewis, T., & Smith, G. (2009). Spatial & spectral variation of seaways. Paper presented at the Proceedings of the 8th European Wave and Tidal Energy Conference.
Bennett, A. F. (1984). Relative Dispersion: Local and Nonlocal Dynamics. Journal of Atmospheric Sciences, 41(11), 1881-1886. doi: 10.1175/1520-0469(1984)041<1881:RDLAND>2.0.CO;2
Beron-Vera, F. J., & LaCasce, J. H. (2016). Statistics of simulated and observed pair separations in the Gulf of Mexico. J. Phys. Oceanogr., 46, 2183-2199. doi: 10.1175/JPO-D-15-0127.1
Berti, S., Dos Santos, F. A., Lacorata, G., & Vulpiani, A. (2011). Lagrangian drifter dispersion in the southwestern Atlantic Ocean. Journal of Physical Oceanography, 41(9), 1659-1672.
Boffetta, G., & Sokolov, I. M. (2002). Statistics of two-particle dispersion in two-dimensional turbulence. Physics of Fluids, 14(9), 3224-3232.
Charney, J. G. (1971). Geostrophic turbulence. Journal of the Atmospheric Sciences, 28(6), 1087-1095.
Corrado, R., Lacorata, G., Palatella, L., Santoleri, R., & Zambianchi, E. (2017). General characteristics of relative dispersion in the ocean. Scientific Reports, 7(1), 46291. doi: 10.1038/srep46291
Cox, C. S., & Munk, W. H. (1954). Measurement of the Roughness of the Sea Surface from Photographs of the Sun’s Glitter. Journal of the Optical Society of America, 44, 838-850.
Crone, T. J., & Tolstoy, M. (2010). Magnitude of the 2010 Gulf of Mexico oil leak. Science, 330(6004), 634-634.
Dräger-Dietel, J., Jochumsen, K., Griesel, A., & Badin, G. (2018). Relative Dispersion of Surface Drifters in the Benguela Upwelling Region. Journal of Physical Oceanography, 48, 2325-2341. doi: 10.1175/JPO-D-18-0027.1
Essink, S., Hormann, V., Centurioni, L. R., & Mahadevan, A. (2019). Can we detect submesoscale motions in drifter pair dispersion? Journal of Physical Oceanography, 49(9), 2237-2254.
GODA, Y. (1977). Numerical Experiments on Statistical Variability of Ocean Waves. Port and Harbour Res. Inst, 18(1).
Hisaki, Y. (2021). Validation of Drifting Buoy Data for Ocean Wave Observation. Journal of Marine Science and Engineering, 9(7). doi: 10.3390/jmse9070729
Kolmogorov, A. N. (1941). The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers. Cr Acad. Sci. URSS, 30, 301-305.
Koszalka, I. M., LaCasce, J. H., & Orvik, K. A. (2009). Relative dispersion in the Nordic seas. J. Mar. Res., 67, 411-433. doi: 10.1357/002224009790741102
Krogstad, H. E., & F. Barstow, S. (1999). Satellite wave measurements for coastal engineering applications. Coastal Engineering, 37(3), 283-307. doi: https://doi.org/10.1016/S0378-3839(99)00030-7
LaCasce, J. (2008). Statistics from Lagrangian observations. Progress in Oceanography, 77(1), 1-29.
LaCasce, J. H. (2016). Estimating Eulerian Energy Spectra from Drifters. Fluids, 1(4). doi: 10.3390/fluids1040033
Lacorata, G., Aurell, E., & Vulpiani, A. (2001). Drifter dispersion in the Adriatic Sea: Lagrangian data and chaotic model. Paper presented at the Annales Geophysicae.
Lumpkin, R., & Elipot, S. (2010). Surface drifter pair spreading in the North Atlantic. J. Geophys. Res., 115, C12017. doi: 10.1029/2010JC006338
Mackay, E. B. (2009). Wave energy resource assessment. University of Southampton.
Manning, J. P., & Churchill, J. H. (2006). Estimates of dispersion from clustered-drifter deployments on the southern flank of Georges Bank. Deep Sea Research Part II: Topical Studies in Oceanography, 53(23-24), 2501-2519.
Michel, M., Obakrim, S., Raillard, N., Ailliot, P., & Monbet, V. (2022). Deep learning for statistical downscaling of sea states. Advances in Statistical Climatology, Meteorology and Oceanography, 8(1), 83-95.
Monin, A. S., & Yaglom, A. M. (2013). Statistical fluid mechanics, volume II: mechanics of turbulence (Vol. 2): Courier Corporation.
Nastrom, G. D., Jasperson, W. H., & Gage, K. S. (1986). Horizontal spectra of atmospheric tracers measured during the Global Atmospheric Sampling Program. J. Geophys. Res., 91, 13 201-213 209. doi: 10.1029/JD091iD12p13201
Normile, D. (2014). Lost at sea: American Association for the Advancement of Science.
Okubo, A. (1970). Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences. Deep Sea Research and Oceanographic Abstracts, 17(3), 445-454. doi: https://doi.org/10.1016/0011-7471(70)90059-8
Özgökmen, T. M., Poje, A. C., Fischer, P. F., Childs, H., Krishnan, H., Garth, C., . . . Ryan, E. (2012). On multi-scale dispersion under the influence of surface mixed layer instabilities and deep flows. Ocean Modell., 56, 16-30. doi: 10.1016/j.ocemod.2012.07.004
Pearson, R. K., Neuvo, Y., Astola, J., & Gabbouj, M. (2016). Generalized Hampel Filters. EURASIP Journal on Advances in Signal Processing, 2016(1), 87. doi: 10.1186/s13634-016-0383-6
Poje, A. (2014). Submesoscale dispersion in the vicinity of the Deepwater Horizon spill. Proc. Natl. Acad. Sci. USA, 111, 12 693-612 698. doi: 10.1073/pnas.1402452111
Richardson, L. F. (1926). Atmospheric diffusion shown on a distance-neighbour graph. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 110(756), 709-737.
Sanson, L. Z., Pérez-Brunius, P., & Sheinbaum, J. (2017). Surface relative dispersion in the southwestern Gulf of Mexico. J. Phys. Oceanogr., 47, 387-403. doi: 10.1175/JPO-D-16-0105.1
Schroeder, K. (2012). Targeted Lagrangian sampling of submesoscale dispersion at a coastal frontal zone. Geophys. Res. Lett., 39, L11608. doi: 10.1029/2012GL051879
Sebille, E. v., Waterman, S., Barthel, A., Lumpkin, R., Keating, S. R., Fogwill, C., & Turney, C. (2015). Pairwise surface drifter separation in the western Pacific sector of the Southern Ocean. J. Geophys. Res. Oceans, 120, 6769-6781. doi: 10.1002/2015JC010972
Taylor, G. I. (1922). Diffusion by continuous movements. Proceedings of the london mathematical society, 2(1), 196-212.
Tseng, R.-S. (2002). On the dispersion and diffusion near estuaries and around islands. Estuarine, Coastal and Shelf Science, 54(1), 89-100.
Weller, R. A., & Anderson, S. P. (1996). Surface Meteorology and Air-Sea Fluxes in the Western Equatorial Pacific Warm Pool during the TOGA Coupled Ocean-Atmosphere Response Experiment, Journal of Climate, 9(8), 1959-1990.
Wu, J. (1994). The sea surface is aerodynamically rough even under light winds. Boundary-Layer Meteorology, 69(1), 149-158. doi: 10.1007/BF00713300
Yanagi, T., Murashita, K., & Higuchi, H. (1982). Horizontal turbulent diffusivity in the sea. Deep Sea Research Part A. Oceanographic Research Papers, 29(2), 217-226. doi: https://doi.org/10.1016/0198-0149(82)90110-8
林志宗（2021）。微型資料浮標觀測波浪及MSS的比對分析與演算流程的改善。未出版之碩士論文，國立中央大學水文與海洋科學研究所，桃園縣。
俞聿修（1999）。隨機波浪及其工程應用）：大連理工大學。
黃暄穎, 張君名., 張志新（2016）。船舶擱淺引致近岸油汙染衝擊評估-以德翔台北事件為例。105年天氣分析與預報研討會， A5-9。

指導教授

錢樺(Hwa Chien)

審核日期

2022-9-23

推文